Brain-computer interface

Abstract

A system and method relating to a brain-computer interface in which a visual stimulus overlaying one or more objects is provided, at least a portion of the visual stimulus having a characteristic modulation. The brain computer interface measures neural response to objects viewed by a user. The neural response to the visual stimulus is correlated to the modulation, the correlation being stronger when attention is concentrated upon the visual stimulus. The visual stimulus includes a feedback element that varies according to a measure of attention on the or each overlaid object.

What is claimed is: 
     
         1 . A method of operating a brain computer interface system, comprising:
 displaying, by a display unit, image data including a plurality of objects;   generating, by a stimulus generator, a visual stimulus having a characteristic modulation corresponding to each of the plurality of objects;   receiving neural signals from a neural signal capture device; determining an object of focus from the plurality of objects based on detecting a correlation between the neural signals and the characteristic modulation of the visual stimulus corresponding to the object of focus; and   displaying a feedback element for the object of focus, wherein the feedback element transitions from an orderless distribution of visual elements to an ordered distribution forming a recognizable shape based on a strength of the correlation between the neural signals and the characteristic modulation.   
     
     
         2 . The method of  claim 1 , wherein the characteristic modulation is selectively applied to a high spatial frequency (HSF) component of the visual. 
     
     
         3 . The method of  claim 1 , wherein the feedback element varies as a linear function of the strength of the correlation. 
     
     
         4 . The method of  claim 1 , wherein the feedback element varies as a non-linear function of the strength of the correlation, and wherein the non-linear function is selected from a sigmoid function, a Rectified Linear Unit (RELU) function or a hyperbolic tangent function. 
     
     
         5 . The method of  claim 1 , wherein the recognizable shape is selected from a reticule, target mark, or cross-hair. 
     
     
         6 . The method of  claim 1 , wherein the characteristic modulation comprises a pseudo-random temporal pattern to reduce temporal overlap between patterns associated with different objects of the plurality of objects. 
     
     
         7 . The method of  claim 1 , wherein the transition from the orderless distribution to the ordered distribution comprises step-wise changes. 
     
     
         8 . A machine comprising:
 at least one processor; and   at least one memory storing instructions that, when executed by the at least one processor, cause the machine to perform operations comprising:   displaying, by a display unit, image data including a plurality of objects;   generating, by a stimulus generator, a visual stimulus having a characteristic modulation corresponding to each of the plurality of objects;   receiving neural signals from a neural signal capture device; determining an object of focus from the plurality of objects based on detecting a correlation between the neural signals and the characteristic modulation of the visual stimulus corresponding to the object of focus; and   displaying a feedback element for the object of focus, wherein the feedback element transitions from an orderless distribution of visual elements to an ordered distribution forming a recognizable shape based on a strength of the correlation between the neural signals and the characteristic modulation.   
     
     
         9 . The machine of  claim 8 , wherein the characteristic modulation is selectively applied to a high spatial frequency (HSF) component of the visual. 
     
     
         10 . The machine of  claim 8 , wherein the feedback element varies as a linear function of the strength of the correlation. 
     
     
         11 . The machine of  claim 8 , wherein the feedback element varies as a non-linear function of the strength of the correlation, and wherein the non-linear function is selected from a sigmoid function, a Rectified Linear Unit (RELU) function or a hyperbolic tangent function. 
     
     
         12 . The machine of  claim 8 , wherein the recognizable shape is selected from a reticule, target mark, or cross-hair. 
     
     
         13 . The machine of  claim 8 , wherein the characteristic modulation comprises a pseudo-random temporal pattern to reduce temporal overlap between patterns associated with different objects of the plurality of objects. 
     
     
         14 . The machine of  claim 8 , wherein the transition from the orderless distribution to the ordered distribution comprises step-wise changes. 
     
     
         15 . A machine-storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:
 displaying, by a display unit, image data including a plurality of objects;   generating, by a stimulus generator, a visual stimulus having a characteristic modulation corresponding to each of the plurality of objects;   receiving neural signals from a neural signal capture device; determining an object of focus from the plurality of objects based on detecting a correlation between the neural signals and the characteristic modulation of the visual stimulus corresponding to the object of focus; and   displaying a feedback element for the object of focus, wherein the feedback element transitions from an orderless distribution of visual elements to an ordered distribution forming a recognizable shape based on a strength of the correlation between the neural signals and the characteristic modulation.   
     
     
         16 . The machine-storage medium of  claim 15 , wherein the characteristic modulation is selectively applied to a high spatial frequency (HSF) component of the visual. 
     
     
         17 . The machine-storage medium of  claim 15 , wherein the feedback element varies as a linear function of the strength of the correlation. 
     
     
         18 . The machine-storage medium of  claim 15 , wherein the feedback element varies as a non-linear function of the strength of the correlation, and wherein the non-linear function is selected from a sigmoid function, a Rectified Linear Unit (RELU) function or a hyperbolic tangent function. 
     
     
         19 . The machine-storage medium of  claim 15 , wherein the recognizable shape is selected from a reticule, target mark, or cross-hair. 
     
     
         20 . The machine-storage medium of  claim 15 , wherein the characteristic modulation comprises a pseudo-random temporal pattern to reduce temporal overlap between patterns associated with different objects of the plurality of objects.